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SARTRE Demonstration System

SARTRE Demonstration System. Eric Chan, Ricardo UK Ltd eric.chan@ricardo.com 21 st October 2012. The research leading to these results has received funding from the European Community's Seventh Framework Programme (FP7/2007-2013) under grant agreement n° 233683. SARTRE Overview.

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SARTRE Demonstration System

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  1. SARTRE Demonstration System Eric Chan, Ricardo UK Ltd eric.chan@ricardo.com 21st October 2012 The research leading to these results has received funding from the European Community's Seventh Framework Programme (FP7/2007-2013) under grant agreement n° 233683.

  2. SARTRE Overview • SARTRE objectives • Develop strategies and technologies for vehicle platoons • Operating on public motorways / highways • No changes to the road and roadside infrastructure • Develop a prototype platooning system • Assess under real world scenarios • Evaluate the environmental, safety, congestion and convenience benefits • Illustrate new business models • Benefits to lead vehicle operators and platoon subscribers • Overall concept • Lead vehicle driven normally by a trained professional driver • Following vehicles have automated driving

  3. Concept Definition • Use Cases • Lead and following vehicle drivers • Road / traffic situations • Traffic modelling • Platoon vehicles • Other non-platoon vehicles • Human factors • Drivers in the platoon • Drivers in other surrounding vehicles • Driving simulator • Safety analysis • Extended standard techniques to cover a system of multiple automated vehicles • Deliberate external malicious threats • Human factors such as operator error/confusion.

  4. Demonstrator System • Five-vehicle road train demonstration system • Mixed vehicle types • Truck, sedan, estate / station wagon, SUV • FH12 truck • S60, V60, XC60 cars • Support a range of user scenarios • Normal use • Joining, leaving, maintaining • Interaction with non-platoon traffic • Constraints • Use existing technologies, or slightly enhanced versions of existing technologies, combined with advanced software • No changes to road infrastructure

  5. Sensors and Sensor Fusion • On-vehicle sensors • Radars: front, side, rear • Lasers (fixed) • Cameras • Lead vehicle driver monitoring sensors • Alco-lock • Camera • Sensor fusion – vehicle • Combine data from sensors • Different sensors have different strengths under different conditions • Sensor fusion – road train • Combine data from vehicles • Form platoon-wide situational awareness

  6. Control Systems, Actuators, V2V Communications • Automated control of vehicle • Longitudinal • Acceleration and braking • Lateral • Steering • Information used • On-vehicle sensors • Shared vehicle data • Actuators build on existing technologies • ACC (Adaptive Cruise Control) • EPAS (Electric Power Assisted Steering) • V2V (Vehicle-to-Vehicle) Communications • Shared real-time vehicle data • Enables coordinated control of road train vehicles with minimal delays

  7. Longitudinal Control • Longitudinal Control has two elements • Using data from the host vehicle sensors • Control of the distance to the preceding vehicle • Using data from the other vehicles • Coordinated control of all platoon vehicles • Transmitted over V2V • Driver can always override • Accelerator pedal • Brake pedal • System will take over atthe end of the override • Harsh braking • Coordinated control allowssystem response withminimal delays

  8. Lateral Control • Lateral Control has two elements • Using data from host vehicle sensors and from preceding vehicles (over V2V) • Creation and tracking of the lead vehicle’s trajectory • Using data from the lead vehicle, transmitted over V2V • Coordinated control of all platoon vehicles • Driver can always override steering wheel • System will take over at the end of the override • Automated steering vs. manual steering • Comparablesteering wheelmovements

  9. Use Cases • Use Case scenarios cover the sequences of actions which the system will have to deal with • Join & leave from rear or side • Back Office or ad-hoc • Maintain platoon • Speed changes • Lane changes • Gap changes • Special scenarios • Driver manual overrides • Degraded modes • Non-platoon vehicles

  10. Human Machine Interface • HMI (Human Machine Interface) components • Touch screen • Status of the SARTRE vehicle • Status of the whole road train • Driver interaction with the system • Voice prompts • Important status updates • Driver keeps eyes on the road • Haptic seat • Alerts driver of status changes • Steering wheel • Natural override of automated lateral system • Accelerator and brake pedals • Natural override of automatedlongitudinal system

  11. Back Office • Register road train availability • Lead vehicle drivers indicate availability and destination of road train • Reservation in a road train • Following vehicle drivers find suitable road trains • Potentially join multiple different road trains in a single journey, depending on destinations • Handles payments and receipts of fees

  12. Conclusions • Five vehicle road train of mixed types • Based on existing technologies with some software enhancements, combined with advanced control software • Up to 90 km/h and 4 m gaps • Some real-world scenarios • Interactions with non-platoon traffic • Tested on test tracks and public roads • Demonstrator system • Not a production implementation • Fuel consumption results • 16% for following vehicles • 8% for lead vehicle

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